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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1776–1800 of 2050 papers

TitleStatusHype
Graphical posterior predictive classifier: Bayesian model averaging with particle GibbsCode0
Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series ClassificationCode0
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts modelsCode0
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsCode0
Model selection with lasso-zero: adding straw to the haystack to better find needlesCode0
Model Selection with Model Zoo via Graph LearningCode0
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective LandscapesCode0
Predictive Multiplicity in ClassificationCode0
Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit ViewCode0
Towards Better Open-Ended Text Generation: A Multicriteria Evaluation FrameworkCode0
Semantic Approach to Quantifying the Consistency of Diffusion Model Image GenerationCode0
An Offline Metric for the Debiasedness of Click ModelsCode0
GTApprox: surrogate modeling for industrial designCode0
Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data StreamsCode0
TensOrMachine: Probabilistic Boolean Tensor DecompositionCode0
Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge TypesCode0
Morphological Segmentation for SenecaCode0
UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise FeaturesCode0
Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding SpecificitiesCode0
Comparison of Anomaly Detectors: Context MattersCode0
Have I done enough planning or should I plan more?Code0
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix FactorisationCode0
Separating common (global and local) and distinct variation in multiple mixed types data setsCode0
HBIC: A Biclustering Algorithm for Heterogeneous DatasetsCode0
A Test of Relative Similarity For Model Selection in Generative ModelsCode0
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